timestamp <- Sys.time()
library(caret)
library(plyr)
library(recipes)
library(dplyr)

model <- "M5Rules"



#########################################################################

library(caret)
library(plyr)
library(recipes)
library(dplyr)
set.seed(1)
training <- SLC14_1(30)
testing <- SLC14_1(100)
trainX <- training[, -ncol(training)]
trainY <- training$y

rec_reg <- recipe(y ~ ., data = training) %>%
  step_center(all_predictors()) %>%
  step_scale(all_predictors()) 
testX <- trainX[, -ncol(training)]
testY <- trainX$y 

rctrl1 <- trainControl(method = "cv", number = 3, returnResamp = "all")
rctrl2 <- trainControl(method = "LOOCV")
rctrl3 <- trainControl(method = "none")

set.seed(849)
test_reg_cv_model <- train(trainX, trainY, method = "M5Rules", trControl = rctrl1,
                           preProc = c("center", "scale"))
test_reg_pred <- predict(test_reg_cv_model, testX)

set.seed(849)
test_reg_cv_form <- train(y ~ ., data = training, method = "M5Rules", trControl = rctrl1,
                          preProc = c("center", "scale"))
test_reg_pred_form <- predict(test_reg_cv_form, testX)

set.seed(849)
test_reg_loo_model <- train(trainX, trainY, method = "M5Rules", trControl = rctrl2,
                            preProc = c("center", "scale"))

set.seed(849)
test_reg_none_model <- train(trainX, trainY, 
                             method = "M5Rules", 
                             trControl = rctrl3,
                             tuneGrid = expand.grid(pruned = "No", 
                                                    smoothed = c("Yes")),
                             preProc = c("center", "scale"))
test_reg_none_pred <- predict(test_reg_none_model, testX)

set.seed(849)
test_reg_rec <- train(x = rec_reg,
                      data = training,
                      method = "M5Rules", 
                      trControl = rctrl1)

if(
  !isTRUE(
    all.equal(test_reg_cv_model$results, 
              test_reg_rec$results))
)
  stop("CV weights not giving the same results")

test_reg_imp_rec <- varImp(test_reg_rec)


test_reg_pred_rec <- predict(test_reg_rec, testing[, -ncol(testing)])

#########################################################################

test_reg_predictors1 <- predictors(test_reg_cv_model)

#########################################################################

tests <- grep("test_", ls(), fixed = TRUE, value = TRUE)

sInfo <- sessionInfo()
timestamp_end <- Sys.time()

save(list = c(tests, "sInfo", "timestamp", "timestamp_end"),
     file = file.path(getwd(), paste(model, ".RData", sep = "")))

if(!interactive())
   q("no")


